In this section, we perform extensive experiments to validate the effectiveness of our proposed model. We use the dataset in [1]}, specifically by splitting a set of three LDR images into two sets that share the same reference image. We generate 150 samples of training data and 30 samples for validation. Our network is trained with an AdamW optimizer. The initial learning rate is set as 1\(e\) -4 and decreases to 1\(e\) -8 with the cosine annealing strategy. we select \( \lambda \)  = 1\(e\) -2 as the weight of structure tensor loss. Random seeds are fixed during all training sessions. We train for 5000 epochs with batch size 4 and set the input image resolution as 256×256 pixels. All experiments are performed on one NVIDIA Geforce RTX 3090 GPU and Intel Core i9-10900k CPU @ 3.70GHz. Our network is programmed on PyTorch.
